Mapping Kochia Patches Using Machine Learning and High-Resolution Satellite Imagery
Bibliographic record
Abstract
Kochia (Bassia scoparia) is a highly competitive weed that reduces crop yields in the Canadian Prairies due to its adaptability and herbicide resistance. Its stress tolerance and resistance to herbicides pose challenges for effective management. In this study, we map annual kochia patches using very high-resolution satellite remote sensing imagery. Field surveys (using drones and GPS) were conducted to collect samples for crop vs kochia classification. Very high-resolution Pleiades NEO (PNEO) imagery (30 cm) was collected for six regions (167 fields, nearly 1000 square kilometers) in Saskatchewan, Canada in August 2024. Kochia and crop areas were labeled, and 26 map features, including spectral bands and indices, were compiled. Through a Random Forest algorithm, survey data and drone imagery were integrated into a model to assess satellite-based mapping effectiveness of mapping kochia using satellite imagery. The workflow, implemented in Google Colab, enabled provincial scalability. Key variables observed to highly influence the robustness of our model included image bands of Green, Red, and Visible Atmospherically Resistant Index. Using PNEO imagery, a 97% prediction accuracy was achieved, highlighting its potential as a scalable tool for monitoring and managing kochia across varying agricultural landscapes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".